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Keyword: Bagging
Tree-Based Ensemble Models, Algorithms and Performance Measures for Classification
An ensemble method is a Machine Learning (ML) algorithm that aggregates the predictions of multiple estimators or models. The purpose of an ensemble module is to provide better predictive performance than any single contributing model. This can be achieved by producing a predictive model with reduced variance using bagging, and bias using boosting. The Tree-Based…
Read More Ensemble Extreme Learning Algorithms for Alzheimer’s Disease Detection
Alzheimer’s disease has proven to be the major cause of dementia in adults, making its early detection an important research goal. We have used Ensemble ELMs (Extreme Learning Models) on the OASIS (Open Access Series of Imaging Studies) data set for Alzheimer’s detection. We have explored various single layered light-weight ELM networks. This is an…
Read More Enhancing Decision Trees for Data Stream Mining
Data stream gained obvious attention by research for years. Mining this type of data generates special challenges because of their unusual nature. Data streams flows are continuous, infinite and with unbounded size. Because of its accuracy, decision tree is one of the most common methods in classifying data streams. The aim of classification is to…
Read More An Evaluation of some Machine Learning Algorithms for the detection of Android Applications Malware
Android Operating system (OS) has been used much more than all other mobile phone’s OS turning android OS to a major point of attack. Android Application installation serves as a major avenue through which attacks can be perpetrated. Permissions must be first granted by the users seeking to install these third-party applications. Some permissions can…
Read More Interpretation of Machine Learning Models for Medical Diagnosis
Machine learning has been dramatically advanced over several decades, from theory context to a general business and technology implementation. Especially in healthcare research, it is obvious to perceive the scrutinizing implementation of machine learning to warranty the rewarded benefits in early disease detection and service recommendation. Many practitioners and researchers have eventually recognized no absolute…
Read More Aggrandized Random Forest to Detect the Credit Card Frauds
From the collection of supervised machine learning technique, an ensemble procedure is used in Random Forest. In the arena of Data mining, there is an excellent claim for machine learning techniques. Random Forest has tremendous latent of becoming a widespread technique for forthcoming classifiers as its performance has been found analogous with ensemble techniques bagging…
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